An Effective and Efficient Congestion Control Scheme for MANET Using COCO Method

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

185

An Effective and Efficient Congestion Control Scheme for

MANET Using COCO Method

K. Devarajan

1

, V. Padmathilagam

2

1,2_{Assistant Professor, Department of Electrical Engg., FEAT, Annamalai University, Annamalai Nagar-608002,}

Chidambaram, Tamil Nadu, India.

Abstract--Due to the mobility of nodes, connection failure between source and destination often occurs in ad-hoc networks. While sending data packets from source to destination, congestion might occur at any node incurring high packet loss and long delay, resulting in performance degradation of the network. Recently, cooperative communication has received tremendous attention in wireless network applications. Most existing works on cooperative communications are based on square measure method, centered on link-layer physical layer problems. In most of the work, the impacts of cooperative communications on network-level higher layer problems like topology management, routing network capability and square measure are ignored. In this report, a Capacity-Optimized Cooperative (COCO) topology management team to increase the network capability in Mobile Ad-hoc Networks (MANETs) by jointly considering each higher layer cooperative communication has been projected. From simulations, it has been observed that topology management team will enhance the network capability in MANETs. COCO method is implemented to enhance the performance of the network topology in MANET using the NS2 simulator.

Keywords- Topology Control, Capacity-Optimized Cooperative (COCO), Mobile Ad-hoc Networks (MANET), Network Capacity, Destination Sequence Distance Vector (DSDV), Ad-hoc On-Demand Vector (AODV), Transports Control Protocol (TCP).

I. INTRODUCTION

Mobil Ad-hoc Networks (MANET)

A mobile ad-hoc network (MANET) is a group of mobile wireless nodes working together to form a network. Such networks can exist without a fixed infrastructure working in an autonomous manner and every mobile device has a maximum transmission power which determines the maximum transmission range of the device. As nodes are mobile, the link connection between the two devices can break depending on the spatial orientation of nodes. Mobile ad-hoc networks have numerous applications in mobile networks, disaster relief systems and military operations. Some of the network constraints in mobile ad-hoc networks are limited bandwidth, low battery power of nodes, and frequent link unreliability due to mobility [1].

Congestion in MANET

Congestion is a situation in communication networks in which too many packets are present in a part of the subnet. Congestion may occur when the load on the network (number of packets send to the network) is greater than the capacity of the network (number of packets a network can handle). Congestion leads to packet losses and bandwidth degradation and waste time and energy on congestion recovery [2]. In internet when congestion occurs it is normally concentrated on a single router, whereas due to the shared medium of the MANET, congestion will not overload the mobile nodes but has an effect on the entire coverage area [3].

Congestion Control in MANET

Congestion control is the main problem in ad-hoc networks. Congestion control is associated with controlling traffic incoming in a telecommunication network. To avoid congestive crumple or link capabilities of the intermediate nodes and networks and to reduce the rate of sending packet congestion control are used extensively [4]. Congestion control and dependability mechanisms are combined with Transports Control Protocol (TCP) to perform the congestion control without explicit feedback about the congestion position and without the intermediate nodes being directly intermittent [4]. So cooperative communication has received tremendous interest for wireless networks.

II. RELATED WORK

SenthilKumaran. T, et al. [5] proposed an Early congestion Detection and Adaptive Routing [EDAPR] in MANET which improves performance in terms of reducing delay, routing overhead and increases packet delivery ratio without incurring any significant additional cost.

YanpingTeng et al. [6] analyzed the major factors affecting TCP performance in ad-hoc networks, gave several typical improved congestion control approaches and compared the network performance of different approaches.

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A mobile agent based congestion control in Ad-hoc On-Demand Vector (AODV) routing protocol is proposed to avoid congestion in ad-hoc network which attains high delivery ratio and throughput with reduced delay.

S. Subburam [8] proposed a Predictive Congestion

Control AODV routing protocol (PCCAODV) in MANETs has lost fewer packets than AODV that are not having a congestion control mechanism. This concept tries to find out non congested alternate path and tries to avoid congestion in the network.

RajniMehta et al. [9] analyzed various congestion control mechanisms using a number of routing protocols in Mobile Ad-hoc Networks (MANET).

Santhoshbaboo. S, et al. [10] worked on implemented and designed COCO topology for MANET networks, caused by the dynamic and random behavior. The Multi-hop mechanism out performs TCP congestion control mechanism and thus is well-matched for applications like Data Transmission in MANET. This technique attains high delivery ratio and throughput with reduced delay of the information delivery and end-to-end delay to balance the traffic load.

III. PROPOSED METHOD

Congestion Control

In this work three forms of congestion control are analyzed

a) Proactive control

b) Reactive control

c) Proactive and Reactive with COCO topology

method

a) Proactive control

In this scheme, the congestion control mechanism is to build reservations of network resources so that resource availability is deterministically guaranteed to admit conversations. It requires each node to keep up a routing table (Destination address, Sequence number and metric) for next hop to reach a destination node and the number of hops to reach destination. Users can be permitted to send data without reservation of resources, but with a possibility that if the network is heavily loaded, the user may receive low utility for network. In proactive control protocols, this work concentrates on Destination Sequenced Distance Vector (DSDV).

DSDV Routing Protocols

Destination Sequence Distance Vector (DSDV) is a proactive routing protocol and is based on the distance vector algorithm. In proactive or table-driven routing protocols, each node constantly maintains up-to-date routes to every other node in the network. Routing information is occasionally transmitted throughout the network in order to keep up the routing table consistency.

The routing table is updated at each node by identifying the change in routing information about all the available destinations with the number of nodes to that particular destination. Also, to offer loop freedom DSDV uses sequence numbers, which is provided by the destination node. In case, if a route has previously existed before traffic arrives, transmission occurs without delay. Otherwise, traffic packets should wait in queue until the node receives routing information related to its destination. However, for highly dynamic network topology, the proactive schemes require a significant amount of resources to maintain routing information up-to-date and reliable. In case of failure of a route to the next node, the node immediately updates the sequence number and broadcasts the information to its neighbors. When a node receives routing information then it checks in its routing table. If it does not find such entry in the routing table, then updates the routing table with routing information it has found. In case, if the node identifies that it has already entered into its routing table, then it compares the sequence number of the received information with the routing table entry and updates the information. If it has a sequence number that is less than that of the received information then it junks the information with the least sequence number. If both of the sequence numbers are the same, then the node maintains the information that has the shortest route or the least number of hops to that destination.

b) Reactive Control

This method is appropriate in reservation less networks. In this case, users have to settle in according to changes in network state and congestion control refers to the way in which a network can allow users to detect changes in network state. Reservation less networks is more prone to congestion. In reactive routing protocols, this work concentrates on Ad-hoc On Demand Distance Vector (AODV).

AODV Routing Protocols

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The forward path sets upon in-between nodes in its routing table with a lifetime association using RREF. When either destination or intermediate node moves, a Routs ERRor (RERR) is sent to the affected source nodes. When a source node receives the Routs Error Message (REM), it can initiate route discovery if the route is still needed.

c) COCO: Capacity Optimized Cooperative Topology

The COCO (Capacity-Optimized Cooperative

topology control scheme) extends the physical layer

cooperative communications from the link-level

perspective to the network-level perspective in MANETs. The Energy Efficient path is preferred between the sources, the relay and the destination nodes. Then the path is verified for the less number of interference on the relay nodes using the COCO Topology scheme. The main purpose of the COCO topology scheme is to set up a path through relay nodes with less number of interferences. Wide research has been done on cooperative communications in which most existing works are paying attention on probability and increasing outage capacities, which are only linked-wide metrics. However, from the network’s point of view, it may not be adequate for the overall network performance, such as the whole network capacity. Therefore, many upper layer aspects of cooperative communications merit additional research, e.g., the impacts on network structure and topology control, especially in mobile ad-hoc networks (MANETs). Indeed, most current studies on MANETs effort to create, adapt and handle a complex network based on traditional simple point-to-point non-cooperative wireless links. Considering the upper layer network capacity and physical layer relay selection, this work proposes a Capacity-Optimized Cooperative (COCO) topology control scheme for MANETs with cooperative communications. Most existing topology control schemes assume that the wireless channel is well known and organized. But, in practice, it is hard to have better knowledge of a dynamic channel. A good topology is the one that can optimize the global network performance while preserve some global graph property (i.e., connectivity). This work presents a novel Capacity-Optimized Cooperative (COCO) topology control scheme for MANETs with co-operative communications. Most existing topology control schemes assume a perfect known wireless channel. However, in practice it is hard to have the perfect knowledge of a dynamic channel. With only channel estimates available in COCO, the topology control problem in MANETs is formulated as a discrete stochastic optimization problem, which can be solved using a stochastic approximation approach. It considers both upper layer network capacity and physical layer relay selection.

One of the main advantages of this iterative approach is that it can track the changing mobile environment to reconfigure the network topology dynamically. From this COCO is the first topology control scheme for MANETs with cooperative communications and noisy channel estimates. With this scheme, relay selection is extended to a network-wide behavior taking network capacity into account. The simulation results show that network capacity can be enhanced substantially in the proposed scheme.

IV. RESULTS AND DISCUSSIONS

The proposed work is implemented using the NS2 simulator tool. Performance analysis is carried out by setting 100 nodes with a grid size of 1000×1000 m. The performance evaluation is based on the different parameters such as packet size, data packets send, data packets received and number of packets delivered. End-to-End delay, Routing overhead and Energy consumption are measured to know the performance of the proposed method.

The simulation settings and parameters are

summarized in the Table.1.

Table.1 Simulation Parameters

Parameter Value

Surface of the network 1000m2

Number of nodes 100

Size of data packet 500 Byte

Eel 50nJ/bit

RTS, CTS, ACK size 30 Bytes

Traffic type Constant

Bit Rate

(CBR)

Routing Protocol AODV, DSDV

Antenna type Omni-Antenna

Channel bandwidth 20kpbs

Initial Energy 2J

Transmission Range 250m

PERFORMANCE METRICS

End-to-End Delay

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Fig.1 Average of End-to End delay

Fig.1 shows the End-to-End delay Vs No. of nodes and compares the End-to-End delay for 100 nodes for various protocols.

End-to-End delay is lower in COCO topology for reactive AODV, whereas COCO topology proactive DSDV has the higher End-to End delay. The lesser the propagation time, transmission time, queuing time and processing time resulting in better End-to-End delay. The lesser time period has been acquired by COCO topology for reactive AODV.

Routing Overhead

It is defined as the percentage of control packets with respect to the received data packets. Each hop of any control packets is computed as a new control packet.

Fig.2 Routing Overhead

Fig.2 shows the routing overhead Vs No. of nodes and compares the routing overhead for 100 nodes for various protocols.

Routing overhead is lower in COCO topology for reactive AODV, whereas COCO topology proactive DSDV has the higher routing overhead. The lesser routing overhead is acquired by COCO topology for reactive AODV.

Energy Consumption

It is the total amount of energy consumed by a process or system.

Fig.3 Energy Consumption

Fig.3 shows the Energy Consumption Vs No.of nodes and compares the Energy consumption for 100 nodes for various protocols.

Energy consumption is less in COCO topology for reactive AODV, whereas COCO proactive DSDV has higher power consumption. So the COCO reactive AODV Energy consumption is best among all the two routing protocol.

V.CONCLUSION

In this research work, a dynamic traffic congestion method using co-operative control scheme, known as COCO considering higher layer network capacity and

physical layer relay selection in cooperative

communications has been presented. The comparative analysis is accomplished on the basis of average End-to-End delay, Energy consumption and Routing overhead using Network Simulator (NS2). The resultant routing protocol gives the finest results based on these parameters and leads to best congestion control mechanisms in MANET. It has been observed that proactive DSDV has a larger time delay, larger routing overhead and high energy consumption. The larger time delay is due to the fact that proactive DSDV is based on the principle of routing table driven which results in larger time delays. Whereas larger routing overhead is due to the fact that the principle of routing table driven consists of larger delay time to identify the routing path. The high energy consumption is due to routing overhead and delay of packet delivery. Using COCO the energy consumption of data delivery, the end-to-end delay and the number of route discovery requests has been reduced. The simulation results have been compared with the existing techniques and it has been observed that the

proposed reactive cooperative communications

techniques have significant impacts on the network capacity.

0 10 20 30

25 50 75 100

E n d -to -E n d d el a y ( sec )

Number of nodes

Average End-to-End delay

PROACTIVE DSDV(EXISTING SYSTEM) PROACTIVE DSDV WITH COCO(PROPOSED SYSTEM) REACTIVE AODV(EXISTING SYSTEM) REACTIVE AODV WITH COCO(PROPOSED SYSTEM) 0 100 200 300 400

25 50 75 100

R o u ti n g Ov er h ea d

Number of nodes Routing Overhead PROACTIVE DSDV(EXISTING SYSTEM) PROACTIVE DSDV WITH COCO(PROPOSE D) REACTIVE AODV(EXISTIN G SYSTEM) REACTIVE AODV WITH COCO(PROPOSE D SYSTEM) 0 50 100 150 200 250

25 50 75 100

E n er g y C o n su m p ti o n

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The COCO for control strategy improves the network capacity in MANETs with reactive AODV using COCO method.

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